Transfer Dynamic Latent Variable Modeling for Quality Prediction of Multimode Processes

Chao Yang, Qiang Liu*, Yi Liu, Yiu-Ming Cheung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

7 Citations (Scopus)


Quality prediction is beneficial to intelligent inspection, advanced process control, operation optimization, and product quality improvements of complex industrial processes. Most of the existing work obeys the assumption that training samples and testing samples follow similar data distributions. The assumption is, however, not true for practical multimode processes with dynamics. In practice, traditional approaches mostly establish a prediction model using the samples from the principal operating mode (POM) with abundant samples. The model is inapplicable to other modes with a few samples. In view of this, this article will propose a novel dynamic latent variable (DLV)-based transfer learning approach, called transfer DLV regression (TDLVR), for quality prediction of multimode processes with dynamics. The proposed TDLVR can not only derive the dynamics between process variables and quality variables in the POM but also extract the co-dynamic variations among process variables between the POM and the new mode. This can effectively overcome data marginal distribution discrepancy and enrich the information of the new mode. To make full use of the available labeled samples from the new mode, an error compensation mechanism is incorporated into the established TDLVR, termed compensated TDLVR (CTDLVR), to adapt to the conditional distribution discrepancy. Empirical studies show the efficacy of the proposed TDLVR and CTDLVR methods in several case studies, including numerical simulation examples and two real-industrial process examples.
Original languageEnglish
Pages (from-to)6061-6074
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Early online date20 Apr 2023
Publication statusPublished - May 2024

Scopus Subject Areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

User-Defined Keywords

  • Dynamic latent variable (DLV)
  • LV regression
  • multimode processes
  • quality prediction
  • transfer learning


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